An Urban Water Extraction Method Combining Deep Learning and Google Earth Engine

Urban water is important for the urban ecosystem. Accurate and efficient detection of urban water with remote sensing data is of great significance for urban management and planning. In this article, we proposed a new method by combining Google Earth Engine (GEE) with a multiscale convolutional neural network (MSCNN) to extract urban water from Landsat images, which can be summarized as “offline training and online prediction” (OTOP). That is, the training of MSCNN is completed offline, and the process of urban water extraction is implemented on GEE with the trained parameters of MSCNN. The OTOP can give full play to the respective advantages of GEE and the convolutional neural network (CNN), and can make the use of deep learning method in GEE more flexible. The proposed method can process the available satellite images with high performance, without data download and storage, and the overall performance of urban water extraction in the test areas is also higher than that of the modified normalized difference water index (MNDWI) and random forest classifier. The results of the extended validation in the other major cities of China also showed that OTOP is robust and can be used to extract different types of urban water, which benefits from the structural design and training of MSCNN. Therefore, OTOP is especially suitable for the study of large-scale and long-term urban water change detection in the background of urbanization.